Analysis of health outcome time series data in epidemiological studies
By G. Touloumi, R. Atkinson, A. Tertre, E. Samoli, Jasmine Schwartz, C. Schindler, J. Vonk, G. Rossi, M. Sáez, Daniel Rabszenko and 1 more. 2004
126 citations recorded by the source corpus.
Subfields: Health, Toxicology and Mutagenesis, Transportation
View Paper: https://www.semanticscholar.org/paper/25635a74d468f1551a9c311ff21a19e9fbacfd33
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In Health, Toxicology and Mutagenesis
- Antonio Gasparrini
- Ben Armstrong
- Michael G. Kenward
- A. Schneider
- Annette Peters
- S. Breitner
- J. Cyrys
- R. Rückerl
In Transportation
Citations in the corpus
- Health effects of particulate air pollution: A review of epidemiological evidence (2011, methodology): “A few years later, Stölzel and colleagues (2007) re-analyzed the data using an extended dataset (September 1995 to August 2001) as well as an alternative modeling approach similar to the APHEA2 study (Touloumi et al., 2004).”
- Distributed lag non-linear models (2010, background): “These studies have taken advantage of the substantial improvements, during the last years, of statistical methods to quantify the short-term effects of air pollution [13, 14].”
- Distributed Lag Linear and Non-Linear Models in R: The Package dlnm (2011, methodology): “The analysis follows a conventional approach used in time series studies on environmental epidemiology (Dominici 2004; Touloumi et al. 2004), where the association between daily ozone and temperature levels on mortality is controlled for other confounding factors like seasonal and long time trend…”
- Reducing and meta-analysing estimates from distributed lag non-linear models (2013, methodology): “Background Research on the health effects of environmental stressors, such as air pollution and temperature, often relies on time series analysis using data from multiple locations, usually cities [1,2].”
- Ambient Temperature and Morbidity: A Review of Epidemiological Evidence (2011, methodology): “Timeseries analysis using daily data was commonly applied, but weekly or monthly data were used in some studies, which may make it difficult to detect acute temperature effects on morbidity (Roger and Francesca 2008; Touloumi et al. 2004).”